Vibration analysis is commonly used in the art of fault-detection in machines. Prior to the emergence of this analysis, faults within machines were difficult to detect before mechanical damage occurred. Such faults often led to costly machine downtimes and costly servicing due to extensive mechanical damage. However, by detecting and analysing vibrations produced by a machine, certain symptoms of impending fault or failure can be detected before major mechanical damage occurs.
The first step in vibration analysis is to gather and record vibration data. The data is then analysed according to known analytical techniques. In recent times, a plurality of analytical techniques have been discovered. Most of these techniques have required separate recordings of vibration data to be made at each measurement location. When this is done on an industrial scale for a large number of machines, the data collection step can become costly simply due to the time involved in the collection. For example, on a large site with 500 machines, each being monitored in the horizontal and vertical directions at each of four points, with three types of measurements being taken at each of these measurement locations, a total of 12,000 recordings must be taken.
The time it takes to acquire each recording depends upon the specific parameters selected. These include, for example, the recording type, the highest frequency of interest or Fmax, and the number of spectral lines. Given these selections, the recording time is governed by relevant physics/signal processing principals. Values of a few seconds are typical. When combined with the time required to walk between all of the measurement locations, to attach the sensor and to wait for it to settle, the collection process can become very time-consuming.
There have been attempts in prior art to address the time-consuming factor of data collection and analysis. U.S. Pat. No. 5,943,634 to Piety et al. describes a data collection, analysis and storage system that minimises data collection time by parameterising time-domain vibration waveforms. Instead of recording data continuously, Piety teaches a technique of recording parameters that could characterise the time-waveform, such as Maximum Peak and Maximum Peak-to-Peak. Time and storage space is made efficient since continuous recording is only performed when the analysed parameters are in alarm.
The main disadvantage of this system is that the resolution and extent of valuable data collected is reduced as a consequence of saving time and space. It is acknowledged in the description of the patent that time data is a ‘highly useful data to assist in the interpretation of certain classes of problems commonly experienced in industry’. However, since ‘saving all of the time data . . . is simply too burdensome to be considered a realistic option’, Piety employs reduced-volume data collection by monitoring key parameters of the time data.
In another U.S. patent to Piety et al., namely U.S. Pat. No. 5,965,819, the time-consuming factor in analysing vibration signals is somewhat addressed. In particular, Piety et al. employs a parallel processing system to simultaneously perform multiple measurements on the detected vibration signal obtained from a single vibration sensor. This setup results in processing that is independent from chain to chain.
It is noted however that the independence of the parallel processing chains comes at a cost—the necessity for each chain to have a complete processing ability results in an increased cost of the device. For each chain, for example, there is a requirement for an analogue-to-digital converter. If three separate analyses are to be performed, the device will require three analogue-to-digital converters.
It is an object of the present invention to provide a method and apparatus which addresses at least one of the abovementioned limitations and/or which at least provides the public with a useful choice.